Research on the principal factors and indicators of urban MICE competitiveness from the perspective of supply: An empirical analysis of 17 CMCA member cities
Abstract
Purpose
Urban MICE competitiveness research consists of two clusters, one that is publicstatisticsbased and another that is questionnairebased. Supplyside research on urban MICE competitiveness is rare. Based on the findings of Chen (2014) and other scholars, the purpose of this paper is to design counterpart statistical indicators to empirically analyze CMCA member cities.
Design/methodology/approach
After calculating the standardized Z value of the original statistical data for 17 CMCA member cities, the authors conducted confirmatory factor analysis for the firstlevel principal components, based on which hierarchical clustering was performed; then, regression analysis was conducted with the MICE profit factor as the dependent variable and the cost factor, tight support factor and facilitating factor as the independent variables to support publishing articles.
Findings
The confirmatory factor analysis showed that the urban MICE competitiveness indicators from the supplyside perspective include the profit factor, cost factor, tight support factor and facilitating factor.
Research limitations/implications
On the basis of research findings from the demand perspective and the literature review, the authors constructed an urban MICE competitiveness indicator system from the perspective of the supply side and conducted principal component analysis. However, because of the inaccessibility of panel data, the current data were only sufficient to conduct the research. If panel data could be acquired, further research could be conducted to perfect the current indicator system for urban MICE competitiveness.
Practical implications
The findings suggest that tourism total income, tourism foreign exchange income, inbound tourist number, number of exhibitions, exhibition area, number of UFI member cities and number of ICCA member cities were the main reason for the gap between different cities’ competitiveness and the reform focus for improving urban MICE competitiveness. The cost factor had a significantly negative influence on urban MICE competitiveness, implying that the higher the average hotel room price and revenue per available room, the less competitive the MICE host city is.
Social implications
The tight support factor exerts a significant positive influence on urban MICE competitiveness from the supplyside perspective, while the cost factor exerts a significant negative influence. The findings suggest that the tourism total income, tourism foreign exchange income, inbound tourist number, number of exhibitions, exhibition area, number of UFI member cities and number of ICCA member cities were the main reason for the gap between different cities’ competitiveness and the reform focus for improving urban MICE competitiveness. The cost factor had a significantly negative influence on urban MICE competitiveness, implying that the higher the average hotel room price and revenue per available room, the less competitive the MICE host city is.
Originality/value
The research bridge the empirical statistics and the questionnairebased perception study on urban MICE tourism image, and advance to construct an empirical statistics based indicator system for urban MICE tourism image.
Keywords
Citation
Chen, J. and Chen, D. (2019), "Research on the principal factors and indicators of urban MICE competitiveness from the perspective of supply: An empirical analysis of 17 CMCA member cities", International Hospitality Review, Vol. 33 No. 1, pp. 3040. https://doi.org/10.1108/IHR1020180020
Download as .RISPublisher
:Emerald Publishing Limited
Copyright © 2019, Jianbin Chen and Danlin Chen
License
Published in International Hospitality Review. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and noncommercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
Because of its high consumption, low price sensitivity, relative stability, high industrial correlation, added value, radiation effect and treatment as a business sector, MICE has attracted much attention from both local governments and academics (Allen et al., 2012). The China MICE Cities Alliance (CMCA) was founded in September 2012 and boasted 17 member cities at the end of 2016.
Urban MICE competitiveness has become a research hotspot as Michael P’s Diamond Model has been widely applied to construct a general urban MICE competitiveness indicator system (Lee, 2004; Zhang, 2014; Cai and Wu, 2014; Yu and Niu, 2012; Piao and Zhang, 2011; Yang, 2010a, b; Lee and Zhan, 2009; Hu, 2009; Lee, 2008; Lee et al.,). The analytical hierarchy process (AHP) has also been widely used to construct general urban MICE competitiveness indicator systems (Ma and Chen, 2013; Ye, 2010; Wang et al., 2009; Lee, 2016; Liu, 2014; Qi, 2007). Factor analysis has been utilized to evaluate urban MICE competitiveness in Guangzhou (Wu, 2009). Fuzzy evaluation (Lee, 2008; Lee et al., 2007; Zhao and Zhao, 2007; Yan and Yu, 2007), IPA (Deng and Lin, 2014; Wang, 2013), SEM (Chen, 2014) and TOPSIS (Wu and Zheng, 2011) have also been utilized to study urban MICE competitiveness. Urban MICE competitiveness has also been studied from the perspective of industrial clusters (Lu, 2012; Cai and Tang, 2011; Zhang et al., 2010; Zhang and Zhang, 2015; Wang, 2014).
Qi (2007) selected 50 indicators to construct an urban MICE competitiveness evaluation system. Based on the available urban statistics regarding facilities, transportation, services, price image, climate and the environment, Zhu (2011) conducted a confirmatory evaluation of urban MICE competitiveness in 17 coastal MICE destination cities. Wang (2015) found that the first five factors influencing Beijing’s MICE development are transportation, professional management level, event theme, sufficient information exposure, climate and the natural environment. Chen (2014), using SEM, found that urban environment, cost, leisure and MICE quality are the principal factors influencing urban MICE image.
Structural reform of the supply side is an important toplevel strategy for the sustainable development of China’s economy in the new era, and its implementation will significantly influence the MICE industry in China. Two published academic articles retrieved from CNKI have discussed China’s MICE industry. Fang (2016) discussed the strategy to promote China’s MICE industry from the supply side. Fan and Wang (2017) described their vision of the supplyside structural reform of the MICE cultural industry.
In summary, urban MICE competitiveness research consists of two clusters: one that is based on public statistics and another that is based on questionnaires. Supplyside research on urban MICE competitiveness is rare. Based on the findings of Chen (2014) and other scholars, this research designed counterpart statistical indicators to empirically analyze CMCA member cities and enriched the research on urban MICE competitiveness from the supply side.
2. Methodology
2.1 Principal component analysis
Principal component analysis is widely utilized in the social sciences for dimension reduction. Each principal component is a linear combination of the original variables, and it is assumed that none of the principal components are correlated. If there are m samples and p variables for each sample, a matrix of n rows and p columns is formed:
The original variables are x_{1}, x_{2}, …, x_{m}, and the new variables are y_{1}, y_{2}, …, y_{p} (p⩽m). Then:
2.2 Regression analysis
Regression analysis is a multivariate statistical technique used to find causal relationships between two or more types of variables. Based on the findings of the principal component analysis, multiple linear models were constructed with the following formula:
β_{0}, β_{1}, β_{2} and β_{3} are the model parameters; Y_{i} is the profit factor score for the ith city; D_{i} is the tight support factor score for the ith city; A_{i} is the cost factor score for the ith city; O_{i} is the facilitating factor score for the ith city; and ξ_{i} is the unknown factor score for the ith city.
3. Data sources and selection of the urban MICE competitiveness indicators
3.1 Selection of the urban MICE competitiveness indicators
According to the research findings of Chen (2014), Qi (2007), Zhu (2011) and Lee, the authors constructed an urban MICE competitiveness indicator system from the perspective of AHP. The firstlevel indicators include the MICE profit and cost factors and the support and facilitating factors. Considering the theoretical foundation and data accessibility, 23 secondlevel indicators were selected (Table I).
3.2 Data sources and processing
The original data sources are the Statistical Bulletin of the National Economic and Social Development of Corresponding Cities (2014), the Urban Statistical Yearbook of China (2015), each city’s tourist bureau website and statistical yearbook (2015) and the China Exhibition Statistics Report (China Exhibition Economy Research Association, 2015).
After calculating the standardized Z value of the original statistical data, the authors conducted confirmatory factor analysis for the firstlevel principal components, based on which hierarchical clustering was performed; then, regression analysis was conducted with the MICE profit factor as the dependent variable and the cost factor, tight support factor and facilitating factor as the independent variables.
4. Analysis of the factors influencing urban MICE competitiveness from the supplyside perspective
4.1 Profit and cost factors of urban MICE competitiveness
A KMO test was conducted on the standardized values, and the KMO value was 0.728, greater than 0.7. Bartlett’s test was significant at the level of 0.000, indicating the feasibility of the factor analysis. The original nine indicators were explained by two factors with eigenvalues greater than 1, and the cumulative variance contribution rate was 86.912 percent, indicating that the two factors have strong explanatory power. The profit and cost factors of urban MICE competitiveness (F_{a}) include total tourism income X_{1}, tourism foreign exchange income X_{2}, inbound tourist persontime X_{3}, average hotel room price X_{4}, revenue per available room X_{5}, number of exhibitions X_{6}, exhibition area X_{7}, number of ICCA member cities X_{8} and number of UFI member cities X_{9} (Table I), the respective factor loadings of which are shown in Table II; the cutoff point is 0.787, far above 0.5. F_{a} includes variables X_{1}, X_{2}, X_{3}, X_{6}, X_{7}, X_{8} and X_{9} as the profit factors, and F_{b} includes X_{4} and X_{5} as the cost factors (Table II).
The tourism profit and cost scores for the CMCA member cities were obtained using the following formula:
According to the above formula, the tourism profit and cost scores for the CMCA member cities were calculated (Table III).
4.2 Tight support factor and facilitating factor
The rotated matrix for factor loading (Table IV) shows that variables X11, X12, X13, X14, X15, X16, X19, X20, X21 and X22, the loadings of which are greater than 0.5, explain a principal component that can be named the tight support factor for MICE (F_{c}) and that variables X10, X17, X18 and X23, the loadings of which are greater than 0.5, explain a principal component that can be named the tight support factor for MICE (F_{d}). The MICE industry needs substantial transit support, and tourism resources and the environment can improve the experience of MICE visitors.
Taking the variance contribution of each factor as the weight, the scores (Table V) for the MICE tight support and facilitating factors of each CMCA member city were calculated according to:
4.3 Comprehensive urban MICE competitiveness
A further factor analysis of the profit and cost factor scores and the tight support and facilitating factor scores was performed to obtain the comprehensive urban MICE competitiveness score for each city (Table V). Based on these results, hierarchical cluster analysis was conducted. The 17 cities could be classified into three classes (Figure 1).
4.4 Regression analysis of the factors influencing urban MICE competitiveness
With the profit factor as the dependent variable and the cost factor, tight support factor and facilitating factor as the independent variables, regression analysis was conducted. The model summary shows that the adjusted R^{2} was 91.6 percent and was significant at the 0.000 level, indicating that 91.6 percent of the variance of the model was explained and at least one of the independent variables entered the intended regression model (Tables VI and VII).
After the test of Cook’s distance and the Mahalanobis distance, two outliers (Shanghai and Guangzhou) were removed from the sample. The tolerance values for each independent variable were between 0 and 1, and the VIF values for each independent variable were between 1.0 and 10, indicating the absence of collinearity problems. The significance level of the facilitating factor, 0.141, indicates that the facilitating factor did not enter the regression model (Table VIII). The final model is as follows:
5. Discussion and findings
The authors work enriched the current research by ① constructing an empirical framework for urban MICE tourism image from the supply side and ② integrating a questionnairebased study and empirical study on urban MICE tourism image that will provide implications for both practitioners and researchers from the supply side, especially in a country where the government and public sectors are the primary organizers of MICE.
5.1 Urban MICE competitiveness indicators from the supplyside perspective
The confirmatory factor analysis showed that the urban MICE competitiveness indicators from the supplyside perspective include the profit factor, cost factor, tight support factor and facilitating factor. The profit factor included variables such as tourism total income, tourism foreign exchange income, inbound tourist number, number of exhibitions, exhibition area, number of UFI member cities and number of ICCA member cities, which indicates the strong radiation effect on the economy. The cost factor included variables such as average hotel room price and revenue per available room. The tight support factor included variables such as the number of exhibition centers, number of 4star hotels, number of 5star hotels, number of travel agents, number of employees in the tertiary industry at yearend, civil aviation traffic volume, GDP, total retail sales of consumer goods and local financial revenue. The facilitating factor included variables such as number of scenic spots above 4 A, railway traffic volume, highway traffic volume and green land area. The results of the principal component analysis show that the initial urban road area at yearend had no significant influence, which requires further research.
5.2 Implications for the principal components influencing urban MICE competitiveness from the supplyside perspective
The tight support factor exerts a significant positive influence on urban MICE competitiveness from the supplyside perspective, whereas the cost factor exerts a significant negative influence. The findings suggest that the total tourism income, tourism foreign exchange income, inbound tourist number, number of exhibitions, exhibition area, number of UFI member cities and number of ICCA member cities were the main reason for the gap between different cities’ competitiveness and the reform focus for improving urban MICE competitiveness.
The cost factor had a significantly negative influence on urban MICE competitiveness, implying that the higher the average hotel room price and revenue per available room, the less competitive the MICE host city is.
5.3 Status quo of the 17 CMCA member cities from the supplyside perspective
The CMCA member cities can be classified into three clusters: international MICE cities, regional MICE cities and local MICE cities. Beijing, Shanghai and Guangzhou are the international MICE cities that are the most competitive in most indicators (Figure 2).
Among regional MICE cities, Xi’an and Chengdu are comprehensive economic and political centers in Northwest and Southwest China, respectively, while Tianjin is the gateway city to Beijing. Hangzhou, Nanjing and Suzhou are compact hinterland cities in the Yangtze River Delta Megalopolis; Qingdao is the gateway city to the Shandong Peninsula Megalopolis, the urban MICE competitiveness of which is neither high nor low, with most indicators being moderate.
Local MICE cities are less competitive, and each has unique problems. Sanya’s MICE profit is lowest but has the highest cost, while Xiamen’s cost is relatively high.
6. Limitations and prospects
Based on research findings from the demand perspective and the literature review, the authors constructed an urban MICE competitiveness indicator system from the perspective of the supply side and performed principal component analysis. However, because of the inaccessibility of panel data, the current data were utilized to conduct the research. If panel data could be acquired, further research could be conducted to refine the current indicator system for urban MICE competitiveness.
Figures
System for evaluating the competitiveness of a city as a MICE destination
Factors  Variables  Indicators 

Urban MICE competitiveness profit and cost factors R1 
Macroprofit E1 
Tourism total income X1//0.1bn yuan (Piao and Zhang, 2011) Tourism foreign exchange income X2//0.1bn dollars (Piao and Zhang, 2011) Inbound tourists X3//10,000 persontimes (Piao and Zhang, 2011) 
MICE cost E2 
Average hotel room price X4//yuan/roomnight (Chen, 2014) Revenue per available room X5//yuan/roomnight^{a} 

MICE profit E3  Number of exhibitions X6 (Qi, 2007; Chen, 2014) Exhibition area X7//10,000 square meters (Oppermann, 1996; Qi, 2007; Chen, 2014) 

E4  Number of ICCA member cities X9//(Chen, 2014) Number of UFI member cities X8//(Chen, 2014) 

Urban MICE competitiveness support and facilitating  Quality of resources E5 
Number of scenic spots above 4A X10//(Piao and Zhang, 2011; Chen, 2014) Number of exhibition centers X11//(Qi, 2007; Chen, 2014) 
factors R2 
Service availability E6 
Number of 4star hotels X12//(Zhu, 2011; Chen, 2014) Number of 5star hotels X13//(Zhu, 2011; Chen, 2014) Number of travel agents X14//(Piao and Zhang, 2011) Number of employees in the tertiary industry at yearend X15//10,000^{a} 
Transportation E7 
Civil aviation traffic volume X16//10,000 persontimes (Zhu, 2011; Chen, 2014) Railway traffic volume X17//10,000 persontimes (Zhu, 2011; Chen, 2014) Highway traffic volume X18//10,000 persontimes (Zhu, 2011; Chen, 2014) Urban road area at yearend X19//10,000 m^{2} (Chen, 2014; Chen, 2014) 

Economic development E8 
GDP X20//0.1bn yuan (Piao and Zhang, 2011) Total retail sales of consumer goods X21//0.1bn yuan (Piao and Zhang, 2011) 

Government support E9 
Local financial revenue X22//0.1bn yuan (Piao and Zhang, 2011)  
Urban environment E10 
Green land area X23//hectare (Chen, 2014) 
Note: ^{a}Selected by the authors from Chen (2014)
Rotated component matrix for MICE profit and cost
Factors  

Indicators  Profit factor F_{a}  Cost factor F_{b} 
Total tourism income X_{1}  0.852  0.277 
Tourism foreign exchange income X_{2}//0.1bn dollars X_{2}  0.926  0.224 
Inbound tourists X_{3}  0.895  0.121 
Average hotel room price X_{4}  0.301  0.942 
Revenue per available room X_{5}  0.225  0.955 
Number of exhibitions X_{6}  0.859  0.311 
Exhibition area X_{7}  0.902  0.271 
Number of UFI member cities X_{8}  0.787  0.478 
Number of ICCA member cities X_{9}  0.812  0.459 
Notes: KMO, 0.728; Approx. χ^{2}, 264.788; df, 36; sig., 0.000, Cronbach’s α for F_{a}, 0.967; Cronbach’s α for F_{b}, 0.956
Tourism profit and cost scores for the CMCA member cities
Comprehensive scores and ranking  

Cities  Scores for profit factor F_{a}  Scores for cost factor F_{b}  Scores  Ranking 
Shanghai  2.53347  1.54408  1.944963  1 
Beijing  2.04450  1.65666  1.685968  2 
Guangzhou  1.17629  0.41807  0.818332  3 
Hangzhou  0.20956  −0.16426  0.078113  5 
Tianjin  0.05812  −0.33947  −0.06165  7 
Nanjing  −0.03341  −0.22558  −0.08382  8 
Chengdu  −0.04913  −0.06875  −0.04872  6 
Suzhou  −0.32384  −0.34955  −0.29175  10 
Xiamen  −0.37432  0.36530  −0.11915  9 
Qingdao  −0.38005  −0.27557  −0.30422  11 
Xi’an  −0.44573  −0.61775  −0.4403  12 
Ningbo  −0.53517  −0.65468  −0.50398  14 
Dalian  −0.57048  −0.74093  −0.54943  15 
Kunming  −0.72398  −0.23867  −0.49838  13 
Guilin  −0.76513  −1.44445  −0.86466  16 
Langfang  −0.90603  −1.16714  −0.86988  17 
Sanya  −0.91468  2.30267  0.108558  4 
Rotated component matrix for MICE potentiality
Factors  

Indicators  Tight support factor F_{c}  Facilitating factor F_{d} 
Number of scenic spots above 4A  0.366  0.890 
Number of exhibition centers  0.891  0.298 
Number of 4star hotels  0.790  0.395 
Number of 5star hotels  0.894  0.295 
Number of travel agents  0.965  0.166 
Number of employees in the tertiary industry at yearend  0.896  0.370 
Civil aviation traffic volume  0.670  0.562 
Railway traffic volume  0.593  0.789 
Highway traffic volume  0.046  0.968 
Urban road area at yearend (10,000 m^{2})  0.494  0.481 
GDP  0.772  0.548 
Total retail sales of consumer goods  0.776  0.604 
Local financial revenue  0.934  0.254 
Green space area  0.577  0.629 
Notes: KMO, 0.721; Approx. χ^{2}, 342.002; df, 78; Sig, 0.000; Cronbach’s α for F_{c}, 0.980; Cronbach’s α for F_{d}, 0.935
Scores for the MICE potentiality of CMCA member cities
Comprehensive scores and ranking  

Cities  Scores for tight support factor F_{c}  Scores for facilitating factor F_{d}  F_{cd} scores  ranking 
Beijing  20.49806  10.80530  10.928063  1 
Shanghai  20.30391  0.93498  10.542442  2 
Guangzhou  0.69133  20.73003  10.253944  3 
Hangzhou  0.10053  −0.04878  0.038373  5 
Tianjin  0.07051  −0.16037  −0.01383  6 
Nanjing  −0.07050  −0.00688  −0.04018  7 
Chengdu  −0.10558  −0.11598  −0.0943  8 
Suzhou  −0.12299  0.47631  0.087614  4 
Xi’an  −0.23275  −0.22716  −0.19868  9 
Qingdao  −0.23833  −0.47664  −0.28226  10 
Ningbo  −0.43223  −0.28007  −0.32318  11 
Dalian  −0.48637  −0.54790  −0.43883  12 
Kunming  −0.51580  −0.72404  −0.51156  13 
Xiamen  −0.58593  −0.73098  −0.55156  14 
Sanya  −0.82587  −10.03450  −0.77878  16 
Langfang  −0.97627  −10.00840  −0.85133  17 
Guilin  −10.07171  −0.58491  −0.76594  15 
Model summary^{b}
Change statistics  

Model  R  R^{2}  Adjusted R^{2}  SE of the Estimate  R^{2} Change  F change  df1  df2  Sig. F change 
1  0.966^{a}  0.934  0.916  0.19647047  0.934  51.564  3  11  0.000 
Notes: ^{a}Predictors: (Constant), cost factor score 2 for analysis 1, support factor score 2 for analysis 1, facilitating factor score 1 for analysis 1; ^{b}dependent variable: profit factor score 1 for analysis 1
ANOVA^{a}
Model  Sum of Squares  df  Mean Square  F  Sig.  

1  Regression  5.971  3  1.990  51.564  0.000^{b} 
Residual  0.425  11  0.039  
Total  6.396  14 
Notes: ^{a}Dependent variable: profit factor score 1 for analysis 1; ^{b}Predictors: (constant), cost factor score 2 for analysis 1, support factor score 2 for analysis 1, facilitating factor score 1 for analysis 1
Coefficients^{a}
Unstandardized coefficients  Standardized coefficients  Collinearity statistics  

Model  B  SE  β  t  Sig.  Tolerance  VIF  
1  Constant  −0.126  0.054  −2.328  0.040  
Cost factor score 2 for analysis 1  −0.371  0.058  −0.573  −6.350  0.000  0.740  1.351  
Support factor score 1 for analysis 1  0.855  0.119  0.886  7.151  0.000  0.393  2.545  
Facilitating factor score 2 for analysis 1  0.225  0.142  0.179  1.584  0.141  0.471  2.123 
Note: ^{a}Dependent variable: profit factor score 1 for analysis 1
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Acknowledgements
Foundation: research on the principal factors influencing urban MICE tourism image from the perspective of systemic markets, taking Guangzhou and Macau as examples, Guangdong Natural Science Foundation (2016A030313707, 2015WTSCX032).